1 Overview

2 Checking to see that the transcript to gene mapping is correct

When you have annotations that are from a different source from your reference you can run into problems (i.e lose genes). Some checks you can do before proceeding:

  1. Look at the dimensions of your count matrix. Do you have ~20k genes present? dim(txi$counts)
  2. When running tximport() you will get a message in your console. If you see something like transcripts missing from tx2gene start troubleshooting.
dim(txi$counts)
## [1] 58735    44

3 Sanity check that metadata matches your expression

It is always a good idea to check if: 1. Do you have expression data for all samples listed in your metadata? 2. Are the samples in your expression data in the same order as your metadata?

### Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))
## [1] TRUE
### Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))
## [1] TRUE
### Check that all samples are in the same order
meta <- meta[colnames(txi$counts),]
all(colnames(txi$counts) == rownames(meta))
## [1] TRUE

4 Run DESeq2

estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing

5 Wald test

Here we subset protein coding genes.

6 DEGreport QC

6.1 Size factor QC - samples 1-15

counts <- counts(dds, normalized = TRUE)
design <- as.data.frame(colData(dds))
degCheckFactors(counts[, 1:5])

6.2 Size factor QC - samples 16-30

degCheckFactors(counts[, 16:30])

6.3 Size factor QC - samples 31-40 (44)

degCheckFactors(counts[, 31:ncol(counts)])

7 Mean-Variance QC plots

7.1 treatment

res <- results(dds)
degQC(counts, design[["treatment"]], pvalue = res[["pvalue"]])

7.2 response

degQC(counts, design[["response"]], pvalue = res[["pvalue"]])

7.3 ER

degQC(counts, design[["er"]], pvalue = res[["pvalue"]])

7.4 tumor_percentage_high

degQC(counts, design[["tumor_percentage_high"]], pvalue = res[["pvalue"]])

8 Covariates effect on count data

mdata <- colData(dds) %>%  as.data.frame()  %>% 
  dplyr::select(treatment, response, er, date_of, tumor_percentage_high)
resCov <- degCovariates(log2(counts(dds)+0.5), mdata)

mdata %>% ggplot(aes(tumor_percentage_high, fill = response)) + 
  geom_bar(position = "dodge2") + 
  ggtitle("Samples in each category")

Samples split equally between tumor_percentage_high for both response types. That allows to control for tumor_percentage_high batch effectively.

9 Covariates correlation with metrics

cor <- degCorCov(mdata)

mdata %>% ggplot(aes(tumor_percentage_high, fill = treatment)) + geom_bar(position = "dodge2")

pre-treatment samples have a larger proportion of higher tumor_percentage - tumor content decreases after treatment, it is harder to sample high purity tumors.

mdata %>% ggplot(aes(date_of, fill = response)) + 
   geom_bar(position = "dodge2") + 
   ggtitle("Distribution of samples across dates of sequencing")

Most pCR samples were sequenced on 20180228 and non-pCR on two other dates. It is important to check the magnitude of DE signal between dates.

10 Sample-level QC analysis

10.1 PCA - treatment

# Use the DESeq2 function
plotPCA(rld, intgroup = c("treatment")) + geom_label_repel(aes(label = name))

10.2 PCA - response

# Use the DESeq2 function
plotPCA(rld, intgroup = c("response"))  + geom_label_repel(aes(label = name))

10.3 PCA - ER

# Use the DESeq2 function
plotPCA(rld, intgroup = c("er"))  + geom_label_repel(aes(label = name))

10.4 PCA - tumor_percentage

# Use the DESeq2 function
plotPCA(rld, intgroup = c("tumor_percentage"))  + geom_label_repel(aes(label = name))

10.5 PCA - tumor_percentage_high

# Use the DESeq2 function
plotPCA(rld, intgroup = c("tumor_percentage_high"))  + geom_label_repel(aes(label = name))

10.6 PCA - date_of

# Use the DESeq2 function
plotPCA(rld, intgroup = c("date_of"))  + geom_label_repel(aes(label = name))

11 Inter-correlation analysis

11.1 Without study_id

# Correlation matrix
rld_cor <- cor(rld_mat)

meta$study_id <- as.factor(meta$study_id)
# Create annotation file for samples
annotation <- meta[, c("treatment", "response", "er", "tumor_percentage_high", "date_of")]

# Change colors
heat.colors <- brewer.pal(6, "Blues")

# Plot heatmap
pheatmap(rld_cor, 
         annotation = annotation, 
         border = NA,
         fontsize = 20)

11.2 Without study_id = top 1000 variable genes

rv <- rowVars(rld_mat)
rv <- order(rv, decreasing = TRUE) %>% head(1000)
rld_mat_1000 <- rld_mat[rv,]
annotation <- meta[, c("treatment", "response", "er", "tumor_percentage_high", "date_of")]

# Change colors
heat.colors <- brewer.pal(6, "Blues")
rld_cor <- cor(rld_mat_1000)
# Plot heatmap
pheatmap(rld_cor, 
         annotation = annotation, 
         border = NA,
         fontsize = 20)

11.3 Wihout study_id = top 500 variable genes

rv <- rowVars(rld_mat)
rv <- order(rv, decreasing = TRUE) %>% head(500)
rld_mat_500 <- rld_mat[rv,]
annotation <- meta[, c("treatment", "response", "er", "tumor_percentage_high", "date_of")]

# Change colors
heat.colors <- brewer.pal(6, "Blues")
rld_cor <- cor(rld_mat_500)
# Plot heatmap
pheatmap(rld_cor, 
         annotation = annotation, 
         border = NA,
         fontsize = 20)

11.4 With study_id

# Correlation matrix
rld_cor <- cor(rld_mat)

meta$study_id <- as.factor(meta$study_id)
# Create annotation file for samples
annotation <- meta[, c("treatment", "response", "er", "tumor_percentage_high", "date_of", "study_id")]

# Change colors
heat.colors <- brewer.pal(6, "Blues")

# Plot heatmap
pheatmap(rld_cor, 
         annotation = annotation, 
         border = NA,
         fontsize = 20)

12 Treatment Post vs Pre - see Table3

13 Response pCR vs non-pCR - see Table4

14 ER : Positive vs Negative - Table 5

15 tumor_percentage_high : High vs Low - Table 6

16 date_of: 20180323 vs 20180228 - Table 7

17 Visualization

Gene example

d <- plotCounts(dds, 
                gene = "ENSG00000130234", 
                intgroup = "treatment", 
                returnData = TRUE)

ggplot(d, aes(x = treatment, y = count, color = treatment)) + 
     geom_point(position = position_jitter(w = 0.1, h = 0)) +
     geom_text_repel(aes(label = rownames(d))) + 
     theme_bw(base_size = 10) +
     ggtitle("ACE2") +
     theme(plot.title = element_text(hjust = 0.5)) +
     scale_y_log10()

# Add a column for significant genes
resTreatment_tb <- resTreatment_tb %>% mutate(threshold = padj < 0.01)

## Volcano plot
ggplot(resTreatment_tb) +
  geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
  ggtitle("Treatment Post vs Pre") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-10,10)) +
  scale_y_continuous(limits = c(0, 2.5)) +
  theme(legend.position = "none",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

# Add a column for significant genes
resResponse_tb <- resResponse_tb %>% mutate(threshold = padj < 0.01)

ggplot(resResponse_tb) +
  geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
  ggtitle("Response pCR vs non-pCR") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-10,10)) +
  scale_y_continuous(limits = c(0, 7.5))+
  theme(legend.position = "none",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

# Add a column for significant genes
resER_tb <- resER_tb %>% mutate(threshold = padj < 0.01)

ggplot(resER_tb) +
  geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
  ggtitle("ER: Positive vs Negative") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-10,10)) +
  theme(legend.position = "none",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

# Add a column for significant genes
resTP_tb <- resTP_tb %>% mutate(threshold = padj < 0.01)

ggplot(resTP_tb) +
  geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
  ggtitle("Tumor_percentage_high: High vs Low") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-10,10)) +
  theme(legend.position = "none",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

# Add a column for significant genes
resDO_tb <- resDO_tb %>% mutate(threshold = padj < 0.01)

ggplot(resDO_tb) +
  geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
  ggtitle("Dafe of: 20180323 vs 20180228") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  scale_x_continuous(limits = c(-10,10)) +
  theme(legend.position = "none",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25)))

18 Heatmaps

# Create a matrix of normalized expression
sig_up <- resTreatment_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resTreatment_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)

row_annotation <- gene_symbol %>% 
                    as_tibble() %>% 
                    dplyr::filter(gene_id %in% sig)

plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>% 
          rownames_to_column(var = "ensembl_gene_id") %>% 
          left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>% 
          drop_na(symbol)

plotmat$ensembl_gene_id <- NULL

plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()

# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")

# Plot heatmap
# color = heat.colors,
pheatmap(plotmat, scale = "row", 
         show_rownames = TRUE,
         border = FALSE,
         annotation = meta[, c("treatment"), drop = FALSE],
         main = "Top 50 Up- and Down- regulated genes in treatment: post vs pre",
         fontsize = 20)

# Create a matrix of normalized expression
sig_up <- resResponse_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resResponse_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)

row_annotation <- gene_symbol %>% 
                    as_tibble() %>% 
                    dplyr::filter(gene_id %in% sig)

plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>% 
          rownames_to_column(var = "ensembl_gene_id") %>% 
          left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>% 
          drop_na(symbol)

plotmat$ensembl_gene_id <- NULL

plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()

# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")

# Plot heatmap
pheatmap(plotmat, 
         scale = "row", 
         show_rownames = TRUE,
         border = FALSE,
         annotation = meta[, c("response"), drop = FALSE],
         main = "Top 50 Up- and Down- regulated genes in Response: pCR vs non-pCR",
         fontsize = 20)

# Create a matrix of normalized expression
sig_up <- resER_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resER_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)

row_annotation <- gene_symbol %>% 
                    as_tibble() %>% 
                    dplyr::filter(gene_id %in% sig)

plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>% 
          rownames_to_column(var = "ensembl_gene_id") %>% 
          left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>% 
          drop_na(symbol)

plotmat$ensembl_gene_id <- NULL

plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()

# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")

# Plot heatmap
pheatmap(plotmat, 
         scale = "row", 
         show_rownames = TRUE,
         border = FALSE,
         annotation = meta[, c("er"), drop = FALSE],
         main = "Top 50 Up- and Down- regulated genes in ER: positive vs negative",
         fontsize = 20)

# Create a matrix of normalized expression
sig_up <- resTP_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resTP_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)

row_annotation <- gene_symbol %>% 
                    as_tibble() %>% 
                    dplyr::filter(gene_id %in% sig)

plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>% 
          rownames_to_column(var = "ensembl_gene_id") %>% 
          left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>% 
          drop_na(symbol)

plotmat$ensembl_gene_id <- NULL

plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()

# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat, 
         scale = "row", 
         show_rownames = TRUE,
         border = FALSE,
         annotation = meta[, c("tumor_percentage_high"), drop = FALSE],
         main = "Top Up/Down-regulated genes in Tumor_percentage_high: high vs low",
         fontsize = 20)

# Create a matrix of normalized expression
sig_up <- resDO_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resDO_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)

row_annotation <- gene_symbol %>% 
                    as_tibble() %>% 
                    dplyr::filter(gene_id %in% sig)

plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>% 
          rownames_to_column(var = "ensembl_gene_id") %>% 
          left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>% 
          drop_na(symbol)

plotmat$ensembl_gene_id <- NULL

plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()

# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")

# Plot heatmap
pheatmap(plotmat, 
         scale = "row", 
         show_rownames = TRUE,
         border = FALSE,
         annotation = meta[, c("response"), drop = FALSE],
         main = "Top 50 Up- and Down- regulated genes in date_of: 20180323 vs 20180228",
         fontsize = 20)

19 R session

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Fedora 32 (Workstation Edition)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.12.so
## 
## locale:
##  [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
##  [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
##  [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] ensembldb_2.14.1            AnnotationFilter_1.14.0    
##  [3] GenomicFeatures_1.42.3      AnnotationDbi_1.52.0       
##  [5] AnnotationHub_2.22.1        BiocFileCache_1.14.0       
##  [7] dbplyr_2.1.1                knitr_1.30                 
##  [9] ggrepel_0.9.1               tximport_1.18.0            
## [11] DEGreport_1.26.0            pheatmap_1.0.12            
## [13] RColorBrewer_1.1-2          forcats_0.5.1              
## [15] stringr_1.4.0               dplyr_1.0.5                
## [17] purrr_0.3.4                 readr_1.4.0                
## [19] tidyr_1.1.3                 tibble_3.1.1               
## [21] ggplot2_3.3.3               tidyverse_1.3.1            
## [23] DESeq2_1.30.1               SummarizedExperiment_1.20.0
## [25] Biobase_2.50.0              MatrixGenerics_1.2.1       
## [27] matrixStats_0.58.0          GenomicRanges_1.42.0       
## [29] GenomeInfoDb_1.26.7         IRanges_2.24.1             
## [31] S4Vectors_0.28.1            BiocGenerics_0.36.1        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1                  backports_1.2.1              
##   [3] circlize_0.4.12               plyr_1.8.6                   
##   [5] lazyeval_0.2.2                ConsensusClusterPlus_1.54.0  
##   [7] splines_4.0.3                 BiocParallel_1.24.1          
##   [9] digest_0.6.27                 htmltools_0.5.1.1            
##  [11] fansi_0.4.2                   magrittr_2.0.1               
##  [13] memoise_2.0.0                 cluster_2.1.0                
##  [15] limma_3.46.0                  ComplexHeatmap_2.6.2         
##  [17] Biostrings_2.58.0             annotate_1.68.0              
##  [19] Nozzle.R1_1.1-1               modelr_0.1.8                 
##  [21] askpass_1.1                   prettyunits_1.1.1            
##  [23] colorspace_2.0-0              blob_1.2.1                   
##  [25] rvest_1.0.0                   rappdirs_0.3.3               
##  [27] haven_2.4.1                   xfun_0.19                    
##  [29] crayon_1.4.1                  RCurl_1.98-1.3               
##  [31] jsonlite_1.7.2                genefilter_1.72.1            
##  [33] survival_3.2-7                glue_1.4.2                   
##  [35] gtable_0.3.0                  zlibbioc_1.36.0              
##  [37] XVector_0.30.0                MatrixModels_0.5-0           
##  [39] GetoptLong_1.0.5              DelayedArray_0.16.3          
##  [41] shape_1.4.5                   SparseM_1.81                 
##  [43] scales_1.1.1                  DBI_1.1.1                    
##  [45] edgeR_3.32.1                  Rcpp_1.0.6                   
##  [47] progress_1.2.2                xtable_1.8-4                 
##  [49] lasso2_1.2-21.1               tmvnsim_1.0-2                
##  [51] clue_0.3-59                   bit_4.0.4                    
##  [53] httr_1.4.2                    ellipsis_0.3.1               
##  [55] farver_2.1.0                  pkgconfig_2.0.3              
##  [57] reshape_0.8.8                 XML_3.99-0.6                 
##  [59] locfit_1.5-9.4                utf8_1.2.1                   
##  [61] labeling_0.4.2                tidyselect_1.1.0             
##  [63] rlang_0.4.10                  later_1.2.0                  
##  [65] munsell_0.5.0                 BiocVersion_3.12.0           
##  [67] cellranger_1.1.0              tools_4.0.3                  
##  [69] cachem_1.0.4                  cli_2.5.0                    
##  [71] generics_0.1.0                RSQLite_2.2.7                
##  [73] broom_0.7.6                   evaluate_0.14                
##  [75] fastmap_1.1.0                 ggdendro_0.1.22              
##  [77] yaml_2.2.1                    bit64_4.0.5                  
##  [79] fs_1.5.0                      nlme_3.1-149                 
##  [81] quantreg_5.85                 mime_0.9                     
##  [83] xml2_1.3.2                    biomaRt_2.46.3               
##  [85] compiler_4.0.3                rstudioapi_0.13              
##  [87] curl_4.3                      png_0.1-7                    
##  [89] interactiveDisplayBase_1.28.0 reprex_2.0.0                 
##  [91] geneplotter_1.68.0            stringi_1.5.3                
##  [93] lattice_0.20-41               ProtGenerics_1.22.0          
##  [95] Matrix_1.2-18                 psych_2.1.3                  
##  [97] vctrs_0.3.7                   pillar_1.6.0                 
##  [99] lifecycle_1.0.0               BiocManager_1.30.12          
## [101] GlobalOptions_0.1.2           conquer_1.0.2                
## [103] cowplot_1.1.1                 bitops_1.0-7                 
## [105] rtracklayer_1.50.0            httpuv_1.6.0                 
## [107] R6_2.5.0                      promises_1.2.0.1             
## [109] MASS_7.3-53                   assertthat_0.2.1             
## [111] openssl_1.4.3                 rjson_0.2.20                 
## [113] withr_2.4.2                   GenomicAlignments_1.26.0     
## [115] Rsamtools_2.6.0               mnormt_2.0.2                 
## [117] GenomeInfoDbData_1.2.4        hms_1.0.0                    
## [119] grid_4.0.3                    rmarkdown_2.5                
## [121] Cairo_1.5-12.2                logging_0.10-108             
## [123] shiny_1.6.0                   lubridate_1.7.10